Using the VidigiPriorityStore to Simplify Model Code Changes with Priority Resources

In vidigi 0.0.5, a new version of VidigiPriorityStore has been added that can be used almost exactly like a resource - reducing the amount of rewriting required to incorporate vidigi into your model.

This allows us to use the pattern

with self.treatment_cubicles.request(priority=patient.priority) as req:

    treatment_resource = yield req

    ### Continue all code in the indented portion that requires the resource, with the resource
    ### automatically being returned to the store when the indented portion completes

Instead of

treatment_resource = yield self.treatment_cubicles.get(priority=patient.priority)

### Continue all code that requires the resource

self.treatment_cubicles.put(treatment_resource)

(even though we are still using a Store behind the scenes)

This minimizes the syntax changes and rewriting that are required when converting an existing model built using resources to a vidigi-compatible state.

When setting up the resources, we simply use the pattern

from vidigi.utils import VidigiPriorityStore, populate_store

...

self.treatment_cubicles = VidigiPriorityStore(self.env)

populate_store(num_resources=g.n_cubicles, # or wherever you are storing your resource counts
               simpy_store=self.treatment_cubicles, # pass in the VidgiStore we created
               sim_env=self.env # include the simpy env this will sit in
               )

from examples.example_7_simplest_case_priority_resource_storewrapper.ex_7_model_classes import Trial, g
from vidigi.prep import reshape_for_animations, generate_animation_df
from vidigi.animation import generate_animation, animate_activity_log
import pandas as pd
import plotly.io as pio
pio.renderers.default = "notebook"
import os

View Imported Code, which has had logging steps added at the appropriate points in the ‘model’ class

import random
import numpy as np
import pandas as pd
import simpy
from sim_tools.distributions import Exponential, Lognormal
from vidigi.resources import VidigiPriorityStore

class g:
    '''
    Create a scenario to parameterise the simulation model

    Parameters:
    -----------
    random_number_set: int, optional (default=DEFAULT_RNG_SET)
        Set to control the initial seeds of each stream of pseudo
        random numbers used in the model.

    n_cubicles: int
        The number of treatment cubicles

    trauma_treat_mean: float
        Mean of the trauma cubicle treatment distribution (Lognormal)

    trauma_treat_var: float
        Variance of the trauma cubicle treatment distribution (Lognormal)

        arrival_rate: float
        Set the mean of the exponential distribution that is used to sample the
        inter-arrival time of patients

    '''
    random_number_set = 42

    n_cubicles = 4
    trauma_treat_mean = 40
    trauma_treat_var = 5

    arrival_rate = 5
    sim_duration = 600
    number_of_runs = 100

class Patient:
    '''
    Class defining details for a patient entity
    '''
    def __init__(self, p_id):
        '''
        Constructor method

        Params:
        -----
        identifier: int
            a numeric identifier for the patient.
        '''
        self.identifier = p_id
        self.arrival = -np.inf
        self.wait_treat = -np.inf
        self.total_time = -np.inf
        self.treat_duration = -np.inf

        # Randomly initialise a patient priority value
        # Lower values will be prioritised - so priority 1 will be seen before priority 2
        if random.uniform(0, 1) < 0.2:
            self.priority = 1
        else:
            self.priority = 2

class Model:
    '''
    Simulates the simplest minor treatment process for a patient

    1. Arrive
    2. Examined/treated by nurse when one available
    3. Discharged
    '''
    # Constructor to set up the model for a run.  We pass in a run number when
    # we create a new model.
    def __init__(self, run_number):
        # Create a SimPy environment in which everything will live
        self.env = simpy.Environment()

        self.event_log = []

        # Create a patient counter (which we'll use as a patient ID)
        self.patient_counter = 0

        self.patients = []

        # Create our resources
        self.init_resources()

        # Store the passed in run number
        self.run_number = run_number

        # Create a new Pandas DataFrame that will store some results against
        # the patient ID (which we'll use as the index).
        self.results_df = pd.DataFrame()
        self.results_df["Patient ID"] = [1]
        self.results_df["Queue Time Cubicle"] = [0.0]
        self.results_df["Time with Nurse"] = [0.0]
        self.results_df.set_index("Patient ID", inplace=True)

        # Create an attribute to store the mean queuing times across this run of
        # the model
        self.mean_q_time_cubicle = 0

        self.patient_inter_arrival_dist = Exponential(mean = g.arrival_rate,
                                                      random_seed = self.run_number*g.random_number_set)
        self.treat_dist = Lognormal(mean = g.trauma_treat_mean,
                                    stdev = g.trauma_treat_var,
                                    random_seed = self.run_number*g.random_number_set)

    def init_resources(self):
        '''
        Init the number of resources
        and store in the arguments container object

        Resource list:
            1. Nurses/treatment bays (same thing in this model)

        '''
        self.treatment_cubicles = VidigiPriorityStore(self.env, num_resources=g.n_cubicles)

    # A generator function that represents the DES generator for patient arrivals
    def generator_patient_arrivals(self):
        # Use an infinite loop here to keep doing this indefinitely while the simulation runs
        while True:
            # Increment the patient counter by 1 (first patient will have an ID of 1)
            self.patient_counter += 1

            p = Patient(self.patient_counter)

            # Store patient in list for later easy access
            self.patients.append(p)

            # Tell SimPy to start up the attend_clinic generator function with this patient
            # (the generator function that will model the patient's journey through the system)
            self.env.process(self.attend_clinic(p))

            # Randomly sample the time to the next patient arriving
            sampled_inter = self.patient_inter_arrival_dist.sample()

            # Freeze this instance of this function in place until the inter-arrival time
            # sampled above has elapsed
            yield self.env.timeout(sampled_inter)

    def attend_clinic(self, patient):
        """
        A generator function that represents the pathway for a patient going through the clinic.

        The patient object is passed in to the generator function so we can extract information
        from / record information to it
        """
        self.arrival = self.env.now

        # ===== LOGGING FOR VIDIGI ANIMATION  ===== #
        self.event_log.append(
            {'patient': patient.identifier,
             'pathway': patient.priority,
             'event_type': 'arrival_departure',
             'event': 'arrival',
             'time': self.env.now}
        )
        # ========================================= #

        # request examination resource
        start_wait = self.env.now

        # ===== LOGGING FOR VIDIGI ANIMATION  ===== #
        self.event_log.append(
            {'patient': patient.identifier,
             'pathway': patient.priority,
             'event': 'treatment_wait_begins',
             'event_type': 'queue',
             'time': self.env.now}
        )
        # ========================================= #

        # Seize a treatment resource when available
        # Note that we must pass in the patient priority
        with self.treatment_cubicles.request(priority=patient.priority) as req:

            treatment_resource = yield req

            # record the waiting time for registration
            self.wait_treat = self.env.now - start_wait

            # ===== LOGGING FOR VIDIGI ANIMATION  ===== #
            self.event_log.append(
                {'patient': patient.identifier,
                    'pathway': patient.priority,
                    'event': 'treatment_begins',
                    'event_type': 'resource_use',
                    'time': self.env.now,
                    'resource_id': treatment_resource.id_attribute
                    }
            )
            # ========================================= #

            # sample treatment duration
            self.treat_duration = self.treat_dist.sample()
            yield self.env.timeout(self.treat_duration)

            # ===== LOGGING FOR VIDIGI ANIMATION  ===== #
            self.event_log.append(
                {'patient': patient.identifier,
                    'pathway': patient.priority,
                    'event': 'treatment_complete',
                    'event_type': 'resource_use_end',
                    'time': self.env.now,
                    'resource_id': treatment_resource.id_attribute}
            )
            # ========================================= #


        # total time in system
        self.total_time = self.env.now - self.arrival

        # ===== LOGGING FOR VIDIGI ANIMATION  ===== #
        self.event_log.append(
            {'patient': patient.identifier,
            'pathway': patient.priority,
            'event': 'depart',
            'event_type': 'arrival_departure',
            'time': self.env.now}
        )
        # ========================================= #


    # This method calculates results over a single run.  Here we just calculate
    # a mean, but in real world models you'd probably want to calculate more.
    def calculate_run_results(self):
        # Take the mean of the queuing times across patients in this run of the
        # model.
        self.mean_q_time_cubicle = self.results_df["Queue Time Cubicle"].mean()

    # The run method starts up the DES entity generators, runs the simulation,
    # and in turns calls anything we need to generate results for the run
    def run(self):
        # Start up our DES entity generators that create new patients.  We've
        # only got one in this model, but we'd need to do this for each one if
        # we had multiple generators.
        self.env.process(self.generator_patient_arrivals())

        # Run the model for the duration specified in g class
        self.env.run(until=g.sim_duration)

        # Now the simulation run has finished, call the method that calculates
        # run results
        self.calculate_run_results()

        self.event_log = pd.DataFrame(self.event_log)

        self.event_log["run"] = self.run_number

        return {'results': self.results_df, 'event_log': self.event_log}

# Class representing a Trial for our simulation - a batch of simulation runs.
class Trial:
    # The constructor sets up a pandas dataframe that will store the key
    # results from each run against run number, with run number as the index.
    def  __init__(self):
        self.df_trial_results = pd.DataFrame()
        self.df_trial_results["Run Number"] = [0]
        self.df_trial_results["Arrivals"] = [0]
        self.df_trial_results["Mean Queue Time Cubicle"] = [0.0]
        self.df_trial_results.set_index("Run Number", inplace=True)

        self.all_event_logs = []

    # Method to run a trial
    def run_trial(self):
        print(f"{g.n_cubicles} nurses")
        print("") ## Print a blank line

        # Run the simulation for the number of runs specified in g class.
        # For each run, we create a new instance of the Model class and call its
        # run method, which sets everything else in motion.  Once the run has
        # completed, we grab out the stored run results (just mean queuing time
        # here) and store it against the run number in the trial results
        # dataframe.
        for run in range(g.number_of_runs):
            random.seed(run)

            my_model = Model(run)
            model_outputs = my_model.run()
            patient_level_results = model_outputs["results"]
            event_log = model_outputs["event_log"]

            self.df_trial_results.loc[run] = [
                len(patient_level_results),
                my_model.mean_q_time_cubicle,
            ]

            # print(event_log)

            self.all_event_logs.append(event_log)

        self.all_event_logs = pd.concat(self.all_event_logs)

my_trial = Trial()

my_trial.run_trial()

4 nurses

my_trial.all_event_logs.head(50)

	patient	pathway	event_type	event	time	resource_id	run
0	1	2	arrival_departure	arrival	0.000000	NaN	0
1	1	2	queue	treatment_wait_begins	0.000000	NaN	0
2	1	2	resource_use	treatment_begins	0.000000	1.0	0
3	2	2	arrival_departure	arrival	3.399660	NaN	0
4	2	2	queue	treatment_wait_begins	3.399660	NaN	0
5	2	2	resource_use	treatment_begins	3.399660	2.0	0
6	3	2	arrival_departure	arrival	8.497645	NaN	0
7	3	2	queue	treatment_wait_begins	8.497645	NaN	0
8	3	2	resource_use	treatment_begins	8.497645	3.0	0
9	4	2	arrival_departure	arrival	8.596678	NaN	0
10	4	2	queue	treatment_wait_begins	8.596678	NaN	0
11	4	2	resource_use	treatment_begins	8.596678	4.0	0
12	5	2	arrival_departure	arrival	8.608025	NaN	0
13	5	2	queue	treatment_wait_begins	8.608025	NaN	0
14	6	2	arrival_departure	arrival	11.359739	NaN	0
15	6	2	queue	treatment_wait_begins	11.359739	NaN	0
16	7	2	arrival_departure	arrival	19.509442	NaN	0
17	7	2	queue	treatment_wait_begins	19.509442	NaN	0
18	8	2	arrival_departure	arrival	22.877356	NaN	0
19	8	2	queue	treatment_wait_begins	22.877356	NaN	0
20	9	2	arrival_departure	arrival	26.653863	NaN	0
21	9	2	queue	treatment_wait_begins	26.653863	NaN	0
22	1	2	resource_use_end	treatment_complete	40.317385	1.0	0
23	1	2	arrival_departure	depart	40.317385	NaN	0
24	5	2	resource_use	treatment_begins	40.317385	1.0	0
25	10	2	arrival_departure	arrival	40.737793	NaN	0
26	10	2	queue	treatment_wait_begins	40.737793	NaN	0
27	2	2	resource_use_end	treatment_complete	42.443230	2.0	0
28	2	2	arrival_departure	depart	42.443230	NaN	0
29	6	2	resource_use	treatment_begins	42.443230	2.0	0
30	4	2	resource_use_end	treatment_complete	48.809628	4.0	0
31	4	2	arrival_departure	depart	48.809628	NaN	0
32	7	2	resource_use	treatment_begins	48.809628	4.0	0
33	3	2	resource_use_end	treatment_complete	51.483457	3.0	0
34	3	2	arrival_departure	depart	51.483457	NaN	0
35	8	2	resource_use	treatment_begins	51.483457	3.0	0
36	11	2	arrival_departure	arrival	71.026558	NaN	0
37	11	2	queue	treatment_wait_begins	71.026558	NaN	0
38	5	2	resource_use_end	treatment_complete	77.447488	1.0	0
39	5	2	arrival_departure	depart	77.447488	NaN	0
40	9	2	resource_use	treatment_begins	77.447488	1.0	0
41	6	2	resource_use_end	treatment_complete	83.962251	2.0	0
42	6	2	arrival_departure	depart	83.962251	NaN	0
43	10	2	resource_use	treatment_begins	83.962251	2.0	0
44	12	2	arrival_departure	arrival	87.458700	NaN	0
45	12	2	queue	treatment_wait_begins	87.458700	NaN	0
46	13	2	arrival_departure	arrival	87.465138	NaN	0
47	13	2	queue	treatment_wait_begins	87.465138	NaN	0
48	7	2	resource_use_end	treatment_complete	95.498040	4.0	0
49	7	2	arrival_departure	depart	95.498040	NaN	0

STEP_SNAPSHOT_MAX = 45
LIMIT_DURATION = g.sim_duration
WRAP_QUEUES_AT = 15

full_patient_df = reshape_for_animations(
    event_log=my_trial.all_event_logs[my_trial.all_event_logs['run']==1],
    every_x_time_units=2,
    entity_col_name="patient",
    step_snapshot_max=STEP_SNAPSHOT_MAX,
    limit_duration=LIMIT_DURATION,
    debug_mode=True
    )

full_patient_df.head(15)

Iteration through time-unit-by-time-unit logs complete 12:37:59
Snapshot df concatenation complete at 12:37:59

	index	patient	pathway	event_type	event	time	resource_id	run	rank	snapshot_time	additional
0	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	0	NaN
1	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	2	NaN
2	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	4	NaN
3	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	6	NaN
4	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	8	NaN
5	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	10	NaN
6	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	12	NaN
7	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	14	NaN
8	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	16	NaN
9	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	18	NaN
10	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	20	NaN
11	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	22	NaN
12	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	24	NaN
13	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	26	NaN
14	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	28	NaN

event_position_df = pd.DataFrame([
                    {'event': 'arrival',
                     'x':  50, 'y': 300,
                     'label': "Arrival" },

                    # Triage - minor and trauma
                    {'event': 'treatment_wait_begins',
                     'x':  205, 'y': 275,
                     'label': "Waiting for Treatment"},

                    {'event': 'treatment_begins',
                     'x':  205, 'y': 175,
                     'resource':'n_cubicles',
                     'label': "Being Treated"},

                    {'event': 'exit',
                     'x':  270, 'y': 70,
                     'label': "Exit"}

                ])

Generate animation using the step-by-step functions

Using the three step-by-step functions allows us to intervene in the produced dataframe and manually take control of the icons in use.

This will allow us to show the high-priority patients with a unique icon so we can see their frequency and how they are handled in the final model.

full_patient_df_plus_pos = generate_animation_df(
    full_entity_df=full_patient_df,
    event_position_df=event_position_df,
    entity_col_name="patient",
    wrap_queues_at=WRAP_QUEUES_AT,
    step_snapshot_max=STEP_SNAPSHOT_MAX,
    gap_between_entities=10,
    gap_between_resources=10,
    gap_between_resource_rows=30,
    gap_between_queue_rows=30,
    debug_mode=True
    )

full_patient_df_plus_pos.sort_values(['patient', 'snapshot_time']).head(15)

Placement dataframe finished construction at 12:37:59

	index	patient	pathway	event_type	event	time	resource_id	run	rank	snapshot_time	additional	x	y_final	label	resource	x_final	row	icon
10186	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	0	NaN	205	175.0	Being Treated	n_cubicles	205.0	0.0	🧔🏼
10187	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	2	NaN	205	175.0	Being Treated	n_cubicles	205.0	0.0	🧔🏼
10188	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	4	NaN	205	175.0	Being Treated	n_cubicles	205.0	0.0	🧔🏼
10189	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	6	NaN	205	175.0	Being Treated	n_cubicles	205.0	0.0	🧔🏼
10190	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	8	NaN	205	175.0	Being Treated	n_cubicles	205.0	0.0	🧔🏼
10191	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	10	NaN	205	175.0	Being Treated	n_cubicles	205.0	0.0	🧔🏼
10192	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	12	NaN	205	175.0	Being Treated	n_cubicles	205.0	0.0	🧔🏼
10193	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	14	NaN	205	175.0	Being Treated	n_cubicles	205.0	0.0	🧔🏼
10194	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	16	NaN	205	175.0	Being Treated	n_cubicles	205.0	0.0	🧔🏼
10195	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	18	NaN	205	175.0	Being Treated	n_cubicles	205.0	0.0	🧔🏼
10196	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	20	NaN	205	175.0	Being Treated	n_cubicles	205.0	0.0	🧔🏼
10197	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	22	NaN	205	175.0	Being Treated	n_cubicles	205.0	0.0	🧔🏼
10198	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	24	NaN	205	175.0	Being Treated	n_cubicles	205.0	0.0	🧔🏼
10199	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	26	NaN	205	175.0	Being Treated	n_cubicles	205.0	0.0	🧔🏼
10200	2	1	1	resource_use	treatment_begins	0.0	1.0	1	1.0	28	NaN	205	175.0	Being Treated	n_cubicles	205.0	0.0	🧔🏼

def show_priority_icon(row):
            if "more" not in row["icon"]:
                if row["pathway"] == 1:
                        return "🚨"
                else:
                    return row["icon"]
            else:
                return row["icon"]

full_patient_df_plus_pos = full_patient_df_plus_pos.assign(
            icon=full_patient_df_plus_pos.apply(show_priority_icon, axis=1)
            )

full_patient_df_plus_pos.head(15)

	index	patient	pathway	event_type	event	time	resource_id	run	rank	snapshot_time	additional	x	y_final	label	resource	x_final	row	icon
0	155	49	2	queue	exit	208.540636	NaN	1	1.0	598	NaN	270	70.0	Exit	NaN	270.0	0.0	🧕🏾
1	155	49	2	queue	treatment_wait_begins	206.540636	NaN	1	27.0	208	NaN	205	305.0	Waiting for Treatment	NaN	95.0	1.0	🧕🏾
2	155	49	2	queue	treatment_wait_begins	206.540636	NaN	1	27.0	210	NaN	205	305.0	Waiting for Treatment	NaN	95.0	1.0	🧕🏾
3	155	49	2	queue	treatment_wait_begins	206.540636	NaN	1	27.0	212	NaN	205	305.0	Waiting for Treatment	NaN	95.0	1.0	🧕🏾
4	155	49	2	queue	treatment_wait_begins	206.540636	NaN	1	27.0	214	NaN	205	305.0	Waiting for Treatment	NaN	95.0	1.0	🧕🏾
5	155	49	2	queue	treatment_wait_begins	206.540636	NaN	1	27.0	216	NaN	205	305.0	Waiting for Treatment	NaN	95.0	1.0	🧕🏾
6	155	49	2	queue	treatment_wait_begins	206.540636	NaN	1	27.0	218	NaN	205	305.0	Waiting for Treatment	NaN	95.0	1.0	🧕🏾
7	155	49	2	queue	treatment_wait_begins	206.540636	NaN	1	27.0	220	NaN	205	305.0	Waiting for Treatment	NaN	95.0	1.0	🧕🏾
8	155	49	2	queue	treatment_wait_begins	206.540636	NaN	1	27.0	222	NaN	205	305.0	Waiting for Treatment	NaN	95.0	1.0	🧕🏾
9	155	49	2	queue	treatment_wait_begins	206.540636	NaN	1	27.0	224	NaN	205	305.0	Waiting for Treatment	NaN	95.0	1.0	🧕🏾
10	155	49	2	queue	treatment_wait_begins	206.540636	NaN	1	27.0	226	NaN	205	305.0	Waiting for Treatment	NaN	95.0	1.0	🧕🏾
11	155	49	2	queue	treatment_wait_begins	206.540636	NaN	1	27.0	228	NaN	205	305.0	Waiting for Treatment	NaN	95.0	1.0	🧕🏾
12	155	49	2	queue	treatment_wait_begins	206.540636	NaN	1	27.0	230	NaN	205	305.0	Waiting for Treatment	NaN	95.0	1.0	🧕🏾
13	155	49	2	queue	treatment_wait_begins	206.540636	NaN	1	27.0	232	NaN	205	305.0	Waiting for Treatment	NaN	95.0	1.0	🧕🏾
14	155	49	2	queue	treatment_wait_begins	206.540636	NaN	1	26.0	234	NaN	205	305.0	Waiting for Treatment	NaN	105.0	1.0	🧕🏾

generate_animation(
        full_entity_df_plus_pos=full_patient_df_plus_pos.sort_values(['patient', 'snapshot_time']),
        event_position_df= event_position_df,
        scenario=g(),
        entity_col_name="patient",
        debug_mode=True,
        setup_mode=False,
        include_play_button=True,
        entity_icon_size=20,
        resource_icon_size=20,
        gap_between_resource_rows=30,
        plotly_height=700,
        frame_duration=800,
        frame_transition_duration=200,
        plotly_width=1200,
        override_x_max=300,
        override_y_max=500,
        time_display_units="dhm",
        display_stage_labels=False,
        add_background_image="https://raw.githubusercontent.com/Bergam0t/vidigi/refs/heads/main/examples/example_1_simplest_case/Simplest%20Model%20Background%20Image%20-%20Horizontal%20Layout.drawio.png",
    )

Output animation generation complete at 12:38:11

Rerun, but using the all-in-one animation function (which will not show different priority icons)

animate_activity_log(
        event_log=my_trial.all_event_logs[my_trial.all_event_logs['run']==1],
        event_position_df= event_position_df,
        scenario=g(),
        entity_col_name="patient",
        debug_mode=True,
        setup_mode=False,
        every_x_time_units=1,
        include_play_button=True,
        entity_icon_size=20,
        resource_icon_size=20,
        gap_between_entities=6,
        gap_between_queue_rows=25,
        gap_between_resource_rows=25,
        plotly_height=700,
        frame_duration=200,
        plotly_width=1200,
        override_x_max=300,
        override_y_max=500,
        limit_duration=g.sim_duration,
        wrap_queues_at=25,
        step_snapshot_max=125,
        time_display_units="dhm",
        display_stage_labels=False,
        add_background_image="https://raw.githubusercontent.com/Bergam0t/vidigi/refs/heads/main/examples/example_1_simplest_case/Simplest%20Model%20Background%20Image%20-%20Horizontal%20Layout.drawio.png",
    )

Animation function called at 12:39:05
Iteration through time-unit-by-time-unit logs complete 12:39:08
Snapshot df concatenation complete at 12:39:08
Reshaped animation dataframe finished construction at 12:39:08
Placement dataframe finished construction at 12:39:08
Output animation generation complete at 12:39:11
Total Time Elapsed: 5.72 seconds